1. Field of the Invention
The present invention relates to a neurocellular automaton and an optimizer employing the same. More specifically, the present invention relates to a neurocellular automaton which can flexibly change the number of neurons, the presence/absence of connections between the neurons, and the weight of the connections, in response to problems, and is formed by a readily producible iterative structure, and an optimizer employing the same.
2. Description of the Background Art
Information processing techniques based on artificial neural networks have been developed in recent years. An artificial neural network (hereafter simply referred to as a neural network), which is formed by connecting a number of units (neurons) performing simple and uniform information processing for simulating a cerebral neural network, is expected to allow extremely flexible processing as compared with a general computer.
However, each neuron can perform only simple information processing. In order to apply an artificial neural network to such complicated processing as that performed by an biological cerebral neural network, therefore, it is necessary to interconnect an extremely large number of neurons with each other. However, software simulation of such interconnection on a general computer is much too time-consuming, and hence impractical. In order to perform the processing at high speed, it is indispensable to implement the neural network with hardware such as electronic circuits.
The feature of a neural network resides in its flexibility. This results from the point that the connection strengths between neurons are changeable. Further, this results from simulation of a biological neural network which is formed by protein etc. and is freely transformable or growable.
On the other hand, hardware is formed by solids such as electronic circuits and remains unchangeable after construction, and hence portions requiring regulation must be previously formed by variable devices. However, a large-scale neural network has an extremely large number of regulation points, and in consideration of wires which are necessary therefore, it has proved difficult to previously build all elements in the network.
The inventors have proposed a neural cellular automaton forming a neural network by growing neurons on a cellular automaton and an optimizer employing the same in Japanese Patent Laying-Open 7-105164 (1995; U.S. Ser. No. 08/316,499). The cellular automaton is formed by regularly arranging and connecting a number of cells having simple structure and function with each other, and intercellular interaction is limited to only neighboring cells. Therefore, the cells are extremely small, and can be concentrated due to a small number of long wires. In this sense, the cellular automaton has a structure suitable for implementation as an integrated electronic circuit.
Despite the simplicity of the individual cells and the regularity of the overall structure, the cellular automaton is applicable to extremely complicated information processing as a whole depending on the contrivance, and its application is wide-ranging.
The aforementioned proposed neural cellular automaton is based on a method of employing signal propagation paths consisting of 3-cell wide trails as axons and dendrites of neurons for propagating various signals through the centers thereof, in accordance with the technique by Codd. In this method, however, it is necessary to insert blank cell parts between the signal propagation paths for preventing interference between the signal propagation paths, and hence 25% of the cells do not participate in operations of the neural network on the average. Further, the signal propagation paths are so wide that the density of the network is reduced and the cellular automaton cannot be effectively used.
In the proposed method, further, the signal strength in the neural network is expressed by state difference, and hence the amount of information to be passed between each cell and its neighbors is increased, leading to complication of state update mechanisms of the cells. Thus, the conventional neural network on the cellular automaton has disadvantages of complicated state update mechanisms of cells and inefficient use of the cellular automaton. These disadvantages are reflected as size increase of the cells and low density of the neural network in implementation of a neurocellular automaton with hardware such as electronic circuits, and lead to an increased physical scale of the device or reduction of the function of the neural network.